Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Desert seismic noise suppression based on multimodal residual convolutional neural network

  • 16 Accesses

Abstract

Seismic exploration is an important means of oil and gas detection, but affected by complex surface and near-surface conditions, and the seismic records are polluted by noise seriously. Particularly in the desert areas, due to the influence of wind and human activities, the complex desert noise with low-frequency, nonstationary and non-Gaussian characteristics is produced. It is difficult to extract effective signals from strong noise using existing denoising methods. To address this issue, the paper proposes a new denoising method, called multimodal residual convolutional neural network (MRCNN). MRCNN combines convolutional neural network (CNN) with variational modal decomposition (VMD) and adopts residual learning method to suppress desert noise. Since CNN-based denoisers can extract data features based on massive training set, the impact of noise types and intensity on the denoised results can be ignored. In addition, VMD algorithm can sparsely decompose signal, which will facilitate the feature extraction of CNN. Therefore, using VMD algorithm to optimize the input data will conducive to the performance of the network denoising. Moreover, MRCNN adopts reversible downsampling operator to improve running speed, achieving a good trade-off between denoising results and efficiency. Extensive experiments on synthetic and real noisy records are conducted to evaluate MRCNN in comparison with existing denoisers. The extensive experiments demonstrate that the MRCNN can exhibit good effectiveness in seismic denoising tasks.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. Bagheri M, Riahi MA, Hashemi H (2017) Denoising and improving the quality of seismic data using combination of DBM filter and FX deconvolution. Arab J Geosci 10(19):440

  2. Baron D, Sarvotham S, Baraniuk RG (2008) Bayesian compressive sensing via belief propagation. IEEE Trans Signal Process 58(1):269–280

  3. Cobelli C (2010) An online self-tunable method to denoise CGM sensor data. IEEE Trans Biomed Eng 57(3):634–641

  4. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

  5. Gan SW, Wang SD, Chen YK, Chen JL, Zhong W, Zhang CL (2016) Improved random noise attenuation using f–x empirical mode decomposition and local similarity. Appl Geophys 13(1):127–134

  6. Hao X, Zhang G, Ma S (2016) Deep learning. Int J Semant Comput 10(03):417–439

  7. He, K. M., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770-778

  8. Levin, A. & Nadler, B. (2011). Natural image denoising: Optimality and inherent bounds, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2833-2840

  9. Li G, Li Y (2016) Random noise of seismic exploration in desert modeling and its applying in noise attenuation. Chin J Geophys 59(2):682–692

  10. Li G, Li Y, Yang B (2015) Effect of wind on seismic exploration random noise on land: modeling and analyzing. J Appl Geophys 119:106–118

  11. Li G, Li Y, Yang B (2017) Seismic exploration random noise on land: modeling and application to noise suppression. IEEE Trans Geosci Remote Sens 55(99):1–14

  12. Li, H., Yang, W. & Yong, X. (2019). Deep learning for ground-roll noise attenuation. 2018 SEG International Exposition and Annual Meeting

  13. Liu L, Ma J (2018) Structured graph dictionary learning and application on the seismic denoising. IEEE Trans Geosci Remote Sens 57(4):1883–1893

  14. Liu W, Cao S, Chen Y (2017) Applications of variational mode decomposition in seismic time-frequency analysis. IEEE J Sel Top Appl Earth Obs Remote Sens 9(8):3821–3831

  15. Makitalo M, Foi A (2012) Optimal inversion of the generalized Anscombe transformation for Poisson–Gaussian noise. IEEE Trans Image Process 22(1):91–103

  16. Michael J, Andrey B, Robert S (2018) Making time-lapse seismic work in a complex desert environment for CO_2 EOR monitoring—design and acquisition. Lead Edge 37(8):598–607

  17. Pilikos G, Faul AC (2017) Bayesian feature learning for seismic compressive sensing and denoising. Geophysics 82(6):O91–O104

  18. Ross ZE, Meier M, Hauksson E, Heaton TH (2018) Generalized seismic phase detection with deep learning. Bull Seismol Soc Am 108(5):2894–2901

  19. Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016). Real-time single image and video superresolution using an efficient sub-pixel convolutional neural network. IEEE Conference on Computer Vision and Pattern Recognition, 1874-1883

  20. Shoulong X, Wutang B, Pizhe L, Zhaotai Q, Prospecting G (2014) Application examples of q compensation in 3d seismic prospecting data processing in aeolian desert areas. Coal Geol China 26(9):61–64

  21. Wang F, Li Y, Kang XT (2017) Aeolian environmental system modeling of desert area in seismic exploration. Prog Geophys 32(6):2545–2551

  22. Yu S, Ma J (2017) Complex variational mode decomposition for slop-preserving denoising. IEEE Trans Geosci Remote Sens 56(99):586–597

  23. Yu, S., Ma, J., & Wang, W. (2018). Deep learning tutorial for denoising. Electrical Engineering and Systems Science

  24. Yuan S, Wang S (2013) Edge-preserving noise reduction based on Bayesian inversion with directional difference constraints. J Geophys Eng 10(2):1742–2132

  25. Yuan S, Wang S, Luo C, Wang T (2018a) Inversion-based 3-D seismic denoising for exploring spatial edges and spatio-temporal signal redundancy. IEEE Geosci Remote Sens Lett 15(11):1682–1686

  26. Yuan S, Liu J, Wang S, Wang T, Shi P (2018b) Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geosci Remote Sens Lett 15(2):272–276

  27. Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2016) Beyond a Gaussian Denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142–3155

  28. Zhang K, Zuo W, Zhang L (2018) FFDNet: toward a fast and flexible solution for CNN based image denoising. IEEE Trans Image Process 27(9):4608–4622

  29. Zhao Y, Li Y, Dong X, Yang B (2019) Low-frequency noise suppression method based on improved DnCNN in desert seismic data. IEEE Geosci Remote Sens Lett 16(5):811–815

  30. Zhong T, Li Y, Wu N, Pengfei N, Baojun Y (2015) Statistical analysis of background noise in seismic prospecting. Geophys Prospect 63(5):1161–1174

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (Under Grants 41730422).

Author information

Correspondence to Yue Li.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Li, Y. & Zhao, Y. Desert seismic noise suppression based on multimodal residual convolutional neural network. Acta Geophys. (2020). https://doi.org/10.1007/s11600-020-00405-4

Download citation

Keywords

  • Residual learning
  • Desert seismic record
  • Noise suppression
  • Variational mode decomposition